Real problems people complain about online, pulled every morning and scored out of 100. Build, validate, or skip. How scoring works
Hey HN, I built Spin Lab: a browser-based interactive explainer for table-tennis spin. It visualizes topspin/backspin, spin rate, ball trajectory, bounce behavior, and why the opponent’s return reacts the way it does. I built it because spin is central to table tennis, but most explanations are either too hand-wavy or too static.Thanks Fable, we miss you
Hacker News3mo agoToolAI
Hey HN - we’re Oskar, Szymon, and Piotr, and we’re building TesterArmy (https://tester.army). TesterArmy is an agentic testing platform that runs end-to-end checks before deployment and in production. Instead of wasting hours on manual testing or maintaining static scripts, we let you specify your tests in natural language and handle everything in between. We've built the platform fully around agents. Our agent will reliably execute the tests, but your coding agent can manage everything in our platform, from defining tests in natural language to running them on your behalf.Check out our demo video: https://www.youtube.com/watch?v=291IkUbPrlk.We started TesterArmy because testing is still far too painful. AI coding tools have made it dramatically faster to write and ship code, but testing is still a bottleneck. Traditional E2E tests are slow to set up and expensive to maintain. Managing auth and test users is painful. Setting up staging environments is painful. Running tests reliably is painful.We think most teams do not actually want to spend their time writing selectors or maintaining test infrastructure. They just want confidence that their core flows work. With TesterArmy, an engineer can sign up, give an agent our CLI, and let it handle creating tests and running them on schedule or on GitHub.When something breaks, TesterArmy alerts your team through Slack or Discord.Over the past few months, we scaled from 0 to 30+ teams using our product every day. We caught bugs in critical flows, including onboarding, checkout, and AI chat. We've got many of our customers migrating from already established competitors to us because of the quality and reliability of our agents.Here are a few of the recent bugs that our agent found (there were quite a lot of them!):1) Timezone bug that affected the booking flow in one of our clients' apps, the dashboard was very complex and hard to catch by a human. 2) Regression in agent orchestration
Hacker News3mo agoToolAI
At Cajal (YC W26) we’re excited to share Talos (https://github.com/cajal-technologies/talos), an open source framework for formal verification of WebAssembly modules in Lean.AI is now writing tons of the code that gets pushed to production. As code generation gets cheaper, verification becomes the bottleneck. We believe in a future where every piece of software comes with a mathematical proof that it does what its author intended - in doing so, eliminating many classes of exploits. Talos is part of the foundation for that.Talos provides a Wasm interpreter optimized for reasoning at the binary level, together with a weakest-precondition calculus layer for proving properties about programs. Because we reason directly about WebAssembly, any language with a Wasm backend is in scope: Rust, C++, Go, C, Swift, Kotlin, Zig, C#, and many more.To make this possible, we use Lean: a programming language and theorem prover that lets you both write software and mathematically prove that it's correct - all in one system. That's what lets Talos double as both an executable interpreter and the formal object Lean reasons about. Lean also integrates with modern AI proving tools, discharging goals automatically via both proof search and direct evaluation.To see Talos in action check out a proof for Stein's GCD algorithm, implemented in the popular Rust crate num-integer: https://github.com/cajal-technologies/talos/blob/main/progra....Our roadmap:- Full Wasm coverage by first passing the official W3C testsuite, then later verifying against SpecTec (formal Wasm spec) - Arbitrary crate verification - any Rust crate that compiles to Wasm should be in scope - Building our proof library codelib, to make verifying increasingly complex programs tractableWe would love to hear the community’s feedback on Talos and comments on the state of formal verification right now. Contributions are also welcome!
Hacker News3mo agoToolAI
The concept for metiq.space came after playing Global Magnates with friends and realizing how fragmented live global data is. ships, aircraft, satellites, ports, weather, hazards, infrastructure, cyber, and public datasets all exist, but they usually live in separate tools and maps.The goal was to build one interactive 3D globe where live public data could be visualized by latitude, longitude, and altitude. Surface data stays on the globe, while aircraft, satellites, and other above surface things can be represented in actual 3D space instead of being flattened onto a map.The outcome is an interactive globe that showcases Earth, air, sea, space, cyber, defense, infrastructure, politics, and the list is continuously growing.Majority of development right now is going into data filtering and deduping.
Hacker News3mo agoToolAI
My team (around 40 people who write code) is evaluating tools for AI-assisted code review. The market appears to be rife with options, so before starting a series of free trials, I’d like to ask a knowledgeable crowd. What tools or services are you using? Do you use them just for code review, or for other purposes as well, such as incident response or branch management? Why did you choose them, and what do you like or dislike about them?
Hacker News3mo agoToolAI
Has anyone here fully swapped Claude/GPT for a local model as their main coding tool, not just for side experiments? If so, please share your setup and performance (e.g tok/s)
Hacker News3mo agoToolAI
Before agentic coding, I always prided myself on how long I could work in a flow state. I was really good at working deeply.Now, with slow agents like Claude, I find myself no longer working deeply.What are you all doing to stay focused?
Hacker News3mo agoToolAI
Driving home from work one day, I wanted to know how many people we knew the names of who lived during the Roman era. Searching around, I found lists of Consuls and officials, but nothing that covered ordinary people or even most people like freedmen and slaves. So I ended up building a pipeline to process the more than 500k Latin inscriptions in the Epigraphic Database Clauss-Slaby https://edcs.hist.uzh.ch/en/ and extract the names of people (and attempt to cluster them, but this is a work in progress).There are databases where Classicists have done this manually for specific regions, Trismegistos https://www.trismegistos.org/ and Latin Inscriptions of the Roman Empire (LIRE) https://pure.au.dk/portal/en/publications/latin-inscriptions... are two major efforts I found. But there doesn't seem to be a project that did what I set out to do, although I have read in some places that it was believed to be possible.I am not a classicist or a web developer, but I have Claude and Gemini and I can sort of read basic Latin - so I set to work. I used LIRE and another database as ground truth and built a pipeline to extract and process the inscriptions to recover the names. The process I developed uses a high end LLM like Sonnet or Gemini Pro to supervise the extraction and tuning process on a regional basis until the obvious error rate is reasonable. For this, so far, reasonable to me means less than 1-2% in the smaller initial samples of 100-500 and no observed systemic issues. The different regions often need different prompts, so this basically became an exercise in letting the higher level AI tune the prompt for the lower level AI. The extraction when measured against LIRE produces an F1 score between 0.64 and 0.87, but take this with a grain of salt.Once I had done a few regions, I wanted to see the work, so I threw together a pretty crude website but as I am not a web developer, it was crude in how it
Hacker News3mo agoToolAI
Hey HN! I'm Zach from Adam (https://adam.new/). We're building AI agents for mechanical CAD software. We’ve built the company on two fundamental beliefs:- AI will be the primary medium for creating mechanical designs just like it is in software today.- The best paradigm for CAD generation is to generate CAD as code (text -> code -> CAD).We’re building CADAM, an open source Text to CAD platform. It's a React app (TanStack Start) with a Supabase backend for auth, database, and file storage. Think of it like AI TinkerCAD.Demo: https://www.youtube.com/watch?v=iESOr7EGWqk Try it: https://adam.new/cadam/What it does:- Generates parametric 3D models from natural language, with support for both text prompts and image references.- Outputs OpenSCAD code with automatically extracted parameters that surface as interactive sliders for instant dimension tweaking- Exports as .STL or .SCAD (plus OBJ, GLB/GLTF, FBX, and DXF)Under the hood:- One agentic endpoint with two modes that swap system prompts and tools: a parametric mode that writes/edits OpenSCAD via a build_parametric_model tool, and a mesh mode that generates 3D textured meshes.- Simple parameter tweaks bypass the model entirely; adjusting a slider does a deterministic regex update on the SCAD source, requiring no LLM call.- Model-agnostic via the Vercel AI SDK: Anthropic (Claude), Google (Gemini), and OpenAI/others through OpenRouter, with adaptive thinking auto-enabled on newer models. Surprisingly, in our evals Gemini 3.1 Pro is the top model.- Runs fully in-browser by compiling OpenSCAD to WebAssembly (in a Web Worker, so the UI never blocks) and rendering with Three.js via React Three Fiber- Supports BOSL, BOSL2, and MCAD libraries, plus custom font support (Geist) for text in modelsFuture improvements:- Support both build123d and CadQuery. This will allow us to move beyond CSG primitives to constraint-driven modeling and provide di
Hacker News3mo agoToolAI
I know, it's kind of weird. What is a veterinarian doing creating an analysis tool for lawn problems?Frankly, the idea was born of my own lawn care struggles. Endless lawn care company fees without any actual improvement. Googling problems and finding generic solutions without regional considerations. One time I overseeded my grass not realizing I had to actually put soil down too.One day, I decided to run lawn pictures through AI and found some pretty helpful information. So I decided with my clinical background (the idea of treating the cause, not just the symptoms), as well as tech savvy, I would create an AI tool where homeowners can upload pictures of their lawn, enter their ZIP code, and get a diagnosis tailored to their location with actionable next steps in just 15 seconds.Completely free. The platform is monetized with affiliate sales (if a user elects to purchase through one of our Amazon or other links) and by selling exclusive rights to individual ZIP codes to lawn care companies seeking warm leads. Users can pursue their own DIY plan, purchase a lawn care subscription service, or contact a local lawn care system.I'd love if you'd test it out, toy with it, try to break it, and give me your feedback. Any feature requests would be super helpful.Thanks! Excited to hear your thoughts.Andrew
Hacker News3mo agoToolAI
Hi HN, I'm Djoumé. I've been a developer for over 20 years, and like a lot of you I've been coding almost exclusively through an agent in the past few months.It's been amazing to vibe code prototypes in any stack, but when it comes to building something reliable/scalable, I couldn't effectively guide the agent unless I knew the technology. And the scariest part is that I'm seeing a lot of my technical skills decreasing due to AI coding.Reflecting on my journey, I also worry about how the new "AI native" generation of software developer is going to acquire technical depth.So I built fata.dev: short daily spaced-repetition sessions for programming skills (Rust, CSS, React, Python, TypeScript, Architecture).You can try it in the browser with no signup: https://fata.app/coursesIt's an offline-first mobile app built with Capacitor, RxDB and Firebase. The first courses were painfully written by hand, but most content is now AI-generated. It takes about 3000 LLM calls to generate a course, and every code samples goes through compilation, linting, unit testing, AI and a final manual review.Would very much appreciate any feedback on the product & website, what works and what could be better. Thanks!
Hacker News3mo agoToolAI
I’m Nick, founder of Drafted (https://www.drafted.ai). We’re training models that generate residential architecture from structured design constraints.Product demo: https://www.youtube.com/watch?v=8QkJ7jNU9y4Residential architecture is still one of the most expensive, slow, and inaccessible creative processes in the world. Designing a custom home typically costs $10,000–$50,000 or more, takes months, and requires making major decisions before most people can even visualize the outcome. As a result, the vast majority of homes are built without direct architectural involvement.Our goal is to teach computers how the built environment works so anyone can imagine, explore, and eventually create physical spaces tailored to them.Today, users can design homes using simple inputs such as: - Square footage targets - Footprint shapes - Lot boundaries - Room placement preferences - Spatial relationships and constraints.Our models generate complete floor plans and matching exterior elevations in seconds. Users can explore designs in both 2D and 3D, iterate instantly, furnish interiors, experiment with materials, and export CAD, PDF, and other files for the rest of the pre-construction process.One of our newest capabilities allows users to draw any footprint shape and generate a complete home layout inside it: https://www.youtube.com/watch?v=wZJhBm7-OHI.Over the past month, more than 120,000 people have used Drafted, generating over 325,000 home designs.If you're building a home, developing property, working in architecture, construction, or AI, we'd love to hear your feedback!
Hacker News3mo agoToolAI
Hi HN! Excited to launch machine0, a CLI that makes it easy to create, provision and snapshot persistent NixOS (& Ubuntu) VMs.You can think of machine0 as a modern VPS provider. VMs stay on unless switched-off (with 99.99% uptime), they have static IPs and HTTPS endpoints, 1-60 vCPU, up to 240GB RAM and optionally GPUs. The CLI provides commands to manage lifecycle, snapshots and also provision the VMs using Nix flakes or Ansible playbooks. VMs are priced by the minute of usage.What makes machine0 unique is that it has first class support for NixOS! In a nutshell, NixOS lets you define your entire OS as code (think Terraform but for your Linux). A flake declares your system state (packages, services, firewall rules, users...) and pins all dependencies via a lockfile. Given the same flake.nix and flake.lock, `nixos-rebuild switch` always produces the exact same system.The NixOS ecosystem is mature, and flakes are expressive: at the system level you can define packages, what's in /etc, firewall rules, users & groups etc. At the user level, you can define your shell, aliases, tmux and vim config. Having your entire environment defined as code makes it easy to audit what's installed and how things are set up. You can rollback by reverting the last commit. And agents can write the code for you and test it against disposable machine0 VMs.If you'd like to dive right in, these commands will get you started: npm install -g @machine0/cli machine0 new my-vm --image nixos-25-11 # create a new nixos VM machine0 provision my-vm ./flake#my-profile # provision it using a nix flake machine0 ssh my-vm # ssh in machine0 stop my-vm # stop the VM machine0 images new my-vm my-snapshot # create a snapshot machine0 new my-clone --image my-snapshot # create a new VM from the snapshot - Demo of installation + NixOS provisioning via Claude Code: https://www.y
Hacker News3mo agoToolAI
Hacker News3mo agoToolAI
Commodity FDM print strength is limited by poor Z-axis layer bonding. Parts crack along Z under stress. MAGMA tries to fix this in software that works on any FDM 3D printer.It's a fork of OrcaSlicer with a new infill type that creates paired U-shaped vertical channels inside the print, plus G-code that injects molten plastic into those channels to bridge Z layer interfaces with continuous plastic.Big caveat: I have a junky Ender 3 and haven't gotten a clean physical print yet. Don't expect this to work out of the box! After months of tinkering, I'm releasing the software so the 3DP community can experiment with nozzles, multi-material, weird hardware, and other print parameters I can't. There's around 40 MAGMA-specific settings to fiddle with, plus some general quality-of-life features (e.g. printing thin infill sections as solid, and a "dual infill shell" feature that applies MAGMA only to the outer shell to save print time).THIS CODE IS ALPHA. Around 50 prints old. The injection G-code is novel. Some printer firmware won't like extruding without movement. In extreme cases it could damage your printer or start a fire. DON'T WALK AWAY WHILE PRINTING.Why MAGMA? "Lava tubes" is a misnomer. Molten rock is magma underground, lava only after it surfaces. The injected tubes are buried inside the print, so "magma tubes" is the correct term.
Hacker News3mo agoToolAI
I'm the developer of Trace, a non-intrusive, shortcut-driven Mac app that records and transcribes your meetings on-device. I know, another meeting transcription app. Please bear with me though, I'm confident that this is at least a little novel.I primarily built Trace for myself. I'd been using MacWhisper, but there was enough fiddling before each call that I'd forget to start it and walk out of an hour-long meeting with nothing written down. So the things I cared about most were that it's quick to activate and stays out of the way. You activate Trace by pressing a global shortcut (configurable), which reveals a small bar at the bottom of your screen (there's also a keystroke and/or option to hide it entirely if you'd rather not see it at all).As I was building it I wanted to bake in a couple of workflows I'd wished for in other transcription apps.1. Mid-meeting you can press another global shortcut to mark a "key moment" and type a note. The note shows up in the resulting transcript inline at that timestamp. I wanted to add this because I kept catching myself thinking "wait, that bit matters" in meetings and reaching to jot it down in a separate app like Obsidian, which I then needed to add context to, which took me out of the meeting. I use it all the time. If I paste the transcript into an LLM afterwards (which I find myself doing more and more these days) the important moments are flagged so it doesn't gloss over them. This is more noticeable in longer meetings with lots of topics. 2. With another keyboard shortcut you can summon a rough live recap (subtitles, basically) to quickly recap what's just been said.Trace uses standard macOS microphone and system recording APIs to capture both sides of the conversation as two separate tracks and then runs the system side through on-device diarization to identify speakers. Right now we only label them as "Speaker 1", "Speaker 2&quo
Hacker News3mo agoToolAI
I built Paca out of pure passion—a free and lightweight Jira alternative written in Go where humans and AI agents work together as equal teammates to plan sprints and assign tasks to each other. It is fully customizable with custom views, fields, and a WASM-based plugin architecture. My team uses it daily for our own development, so it will be continuously maintained and completely free forever
Hacker News3mo agoToolAI
I haven't found a way to delete all chats in bulk like you can on Chatgpt. With Claude, you have to scroll to the bottom, select everything, and delete. The problem is, if you have a lot of chats, it becomes impossible. I created this script. It does it alone. I hope it helps someone.(conversations disappear from the UI slowly, over several minutes, and remember to keep the tab open until the console shows "Finished", refreshing away from the page can stop the deletion process.)
Hacker News3mo agoToolAI
Hey all, I built StackScope, a crawler/catalogue that looks at new product launches and shows what they were built with.It watches launches from Product Hunt, Show HN, and PeerPush, then crawls the public site behind each one. The goal is to show what people actually launched with: hosting, frameworks, analytics, DNS, security headers, legal pages, AI-builder signals, and other public clues.I started building it because most stack-detection sites look at the web as a whole. I was more interested in the current indie launch scene: what people are choosing right now, at the point they first put something in public.A few implementation details: it runs on .NET, uses Playwright for rendered pages, and has a first-party fingerprint catalogue rather than one copied from Wappalyzer/etc. robots.txt is honoured, and the bot identifies itself.Frustratingly, I am still waiting for verified bot status from Cloudflare and currently that knocks out about 10% of all sites.There is also a private readiness check: paste a URL, get the same style of report, fix things, and recrawl. No account or email needed.I'd be interested in feedback on the usefulness of this, the methodology, and any obvious false positives.Jonathan.
Hacker News3mo agoToolAI
We’re Connor and Ambar from BitBoard (https://bitboard.work). BitBoard is an agentic analytics workspace. We give you the infrastructure and visualization layer to analyze data with AI.Today, we’re launching dashboards that you and your agents can work on together. You can connect your coding agent or AI chat to BitBoard and build live reporting. Here’s a demo: https://www.youtube.com/watch?v=HPl0K565a7c.AI tools treat data analysis as ephemeral, making it hard to report or collaborate. Legacy BI tools weren’t intended for AI users, so they bolt on chatbots and can’t offer meaningful control to your agents. Software can now make far more of a business legible than BI ever could, but neither legacy BI nor chat bots are built to handle it.Our original product was AI agents for administrative tasks in healthcare (https://news.ycombinator.com/item?id=44237769), but customers kept pulling us toward their data analysis problems: queries scattered across disparate sources, spreadsheets floating everywhere. We kept building tooling for addressing that, and at a certain point those tools were becoming our product.We ran into several problems. Agents made bad inferences because they had no context on the business. They couldn't be trusted to make decisions because nothing checked their work. And anything one agent or one person figured out was invisible to everyone else. In BitBoard, humans and agents interact with the same data primitives but get tools designed for their own work.We’re building dashboards to make the human reading experience better. These dashboards progressively use intelligence - starting from code or SQL queries and leading to full embedded apps. Humans and agents will need to agree on methods to interpret data, so we’re letting both contribute to canonical sources, entities, and measures (using your favorite semantic model or ours). Every answer comes with provenance, and the same call with the same parameters
Hacker News3mo agoToolAI